San Francisco, CA, USA
Nov 1, 2022   |  By Kensu
Kensu announces its partnership with Collibra, the Data Intelligence company, and the availability of an integration between the two solutions. Kensu's observability capacities will enrich Collibra's Catalog with clean, trustworthy, and curated information to enable business users and data scientists to make business decisions based on reliable data.
Sep 12, 2022   |  By Kensu
Supporting the data community with the first free solution to monitor data pipelines and unleash the potential of this new category.
Jul 28, 2022   |  By Kensu
... And announces Seed Funding of $4.2M to accelerate Kensu's growth, open US Headquarters and continue to set the standard for data observability at the source.
Jul 12, 2022   |  By Zack Izham
In this article, we discuss how you can avoid data pipeline breakdowns thanks to total observability through the use of dbt complemented with Kensu. Data quality problems tend to manifest in many ways. Here is an example.
May 25, 2022   |  By Andy Petrella
Before the data era, data engineers and data scientists had few resources, few technologies, and few data to build something from. But they also had little pressure from the business to create new values, and above all, it was easier to find some time to write, check and implement their applications. It had the advantage of better control of quality.
May 5, 2022   |  By Andy Petrella
Data has become the lifeblood of most organizations. Yet, despite using data almost daily to make critical business decisions, few organizations have complete visibility into the health and usage of their data. Moreover, as the acceleration of data usage has increased, so too has the complexity of data systems, increasing the risks of data-related issues and making it even more difficult to identify and resolve issues related to data quickly.
May 3, 2022   |  By Andy Petrella
In our last article, we introduced the topic of SLAs (Service Level Agreements) and how they are necessary within organizations to help both consumers and producers agree on expectations around data usage and quality. Not only do SLAs provide visibility into what needs to be achieved to ensure data reliability and avoid surprises, but SLAs also create communication flows between consumers and producers that help ensure an alignment on expectations.
Apr 27, 2022   |  By Andy Petrella
As far back as the 1920s, Service Level Agreements (SLA) were used to guarantee a certain level of service between two parties. Back then, it was the on-time delivery of printed AR reports. Today, SLAs define service standards such as uptime and support responsiveness to ensure reliability. The benefit of having an SLA in place is that it establishes trust at the start of new customer relationships and sets expectations.
Apr 26, 2022   |  By Andy Petrella
When explaining what Data Observability Driven Development (DODD) is and why it should be a best practice in any data ecosystem, using food traceability as an analogy can be helpful. The purpose of food traceability is to be able to know exactly where food products or ingredients came from and what their state is at each moment in the supply chain. It is a standard practice in many countries, and it applies to almost every type of food product.
Apr 15, 2022   |  By Andy Petrella
“Without clean data, or clean enough data, your data science is worthless.” Michael Stonebraker, adjunct professor, MIT AI is one of the fastest-growing and most popular data-driven technologies in use. Nine in ten of Fortune 1000 companies currently have ongoing investments in AI. So you may be wondering: how could there possibly be another AI winter?
Oct 11, 2022   |  By Kensu
Quickly detect, troubleshoot, and prevent the propagation of a wide range of data incidents through Data Observability, a set of best practices that allow data teams to gain greater visibility of data and its usage. If you're a data engineer, ML engineer, or data architect, or if the quality of your work depends on the quality of your data, this book shows how to focus on the practical aspects of introducing Data Observability in your day-to-day work.

Our low latency data observability solution alerts about data issues, prevents their propagation, and highlights which applications are impacted.

To foster a data-driven culture, automation of data observability at scale is essential. The best way to achieve this is through what is called Data Observability Driven Development [DODD] which implies observable information is produced by the applications.

The method is a paradigm shift that allows data teams and data usage to scale efficiently. DODD is done from within the applications to enable data projects with synchronized observability, continuous validation, and contextual observability.

Data Observability is for everyone:

  • For Data Scientists: Remain confident about the models in production by being notified as soon as performance is deviating.
  • For Data Engineers: Save time and trouble by easily increasing your visibility and control over data in production.
  • For Heads of Data: Increase the productivity of your team by reducing the resources required to maintain existing data applications.
  • For Analysts: Increase trust in existing reports by being immediately alerted as soon as data quality is out of range.

Trust what you deliver.